Assignment

Data is around us, knowing how to capture it, use it and present it is an important skill to my study of Cognitive Science.

In the autumn semester of 2026, I’ve attended the elective course titled: “Curating Data” at Aarhus University.

Coming from Cognitive Science, I believed that data is somewhat neutral as the usual practice was to use the data in order to explain or prove some phenomena, and although the importance of considering the capturing methods and the tools used for experiments were emphasized for the experiments conducted by the students, the main source of possible biases were placed on the use of data and its transformations and modelling.

Throughout “Curating Data” I met many new ways of understanding data, data structures, semantic networks and visualization.
In this course the idea of “capta”, emphasized by Kitchin (Kitchin 2022), for data was introduced to me. I believe that this concept was a core component of the shift in my understanding of data given that maybe data is not neutral and that the methods previously used to capture it matter much more than I previously thought.

Throughout the course 3 assignments were made that will be described in this exhibition, each dealing with a different aspect of curating data. The first assignment was regarding classification, the second about semantic networks and the third about visualization.
Each assignment will be presented as a sub-process of the whole semester and will be introduced in detail through this exhibition with a focus on my conclusions from them.

Curatorial statement

Assignment 1
Classification

Objective: "Datafy" around 20 objects and create a dataset with at least 5 variables, and write a critical report on the process and outcome.

Tools: Microsoft Excel, Camera, Microsoft Paint

Solution: A collection of beverage containers (both carbonated drinks and alcoholic beers alike) was created in Excel, with an essay reflecting on the collection process, the variables and decision making processes involved in the collection.

Created by Márton Ferenc Péterdi

Assignment 2
Semantic Networks

Objective: Create a Wikidata page of any person selected from the Dekoloniale website (in groups) and write a methodology report (individual)

Tools: Dekoloniale, a collection of biographies (https://dekoloniale.de/en/map/stories)
Wikidata account

Solution: The Wikidata page "Liao Huanxing (Q136450200)" was created in collaboration with Clara Holst. The page contains information from the biography of Liao Huanxing, using "properties" in "statements", Wikidata's underlying semantic system. In addition a report was written describing the decision making process behind selection of data as well as the data structurization challenges and critical reflections.

Assignment 3
Visualization

Objective: Find a dataset and create two visualizations from it, one that could be used for analytical reasoning and one with a feminist/ critical data studies approach.

Tools: Kaggle for finding a dataset (https://www.kaggle.com/), PowerBI and Microsoft Excel for visualization

Solution: A dataset about Brazilian organized crime conflicts from 1997 to 2024 was found on Kaggle, this dataset was used in both Power BI and Microsoft Excel in order to create visuals that can be a base for reasoning for both objectives. A reflection paper was also written describing the visualizations, the motivation, the design choices and how certain information can be suppressed.

Here, I have summarized what I believe I have learned from this course through the assignments.

From Assignment 1 the main takeaway would be that data (or perhaps "capta") is all around us, the way it is collected matters and design choices that seem perhaps seem obvious could include bias in datasets.(Kitchin 2022)

Assignment 2 greatly highlights how semantic systems underlying large libraries of information works. These libraries are used by countless of people every day so having a strong foundation is a must for systems like these. In addition it shows the accessibility of Wikidata to the average person, as well as how collaboration/ teamwork can help to build a network of information, as well as the problems that come with it.

For me, Assignment 3 was the most impactful. The main conclusion is that visualizing data is not just a tool, but a responsibility because hiding information from people and misrepresenting data is not necessarily hard.(D’Ignazio & Klein 2020, Bowker & Star 2000) My assignment highlights how the reader can feel informed and content with the reports on certain problem, while being completely unaware of the actual problem. (How the first 3 charts seem correct and understandable, but in light of the "unknown" variable it is obviously misrepresents the data, this was done intentionally).

My understanding of this course is that it is a process. The course itself had a certain steps that had to be taken to reach a desired result. The assignments can be seen as sub-processes as they have all of these parts similarly to the course, just contained within themselves. Below there is a description of the assignments that follows the following system: Title of Assignment, Objective ==> A description of the task given to the students, Tools ==> Platforms, items or software used to create the assignment, Solution ==> A brief description of what the result of the assignment is, that was created using the Tools.

The goal of the course could be understood as to cultivate a form of “practical” wisdom: the ability to evaluate how knowledge operates across contexts, make informed judgements, and recognize the social, ethical, and political dimensions of data practices.

Further below, I will explain what is the "take-away"/ conclusion that I drew from completing these assignments. These are based on what I have learned during the course and how they align with the desired result.

The definition on the right is taken from the Cambridge Dictionary (https://dictionary.cambridge.org/dictionary/english/process)